class_docstr: Cost function of a clustering problem.
  
  Parameters
  ----------
  points: np.ndarray
      k x n array where k is the number of points and n their coordinates
  num_clusters: int
      number of clusters to find
docstr: Sum of minimum squared distances to closest centroid
  centers must be of size num_clusters x n
docstr_tok: ["Sum","of","minimum","squared","distances","to","closest","centroid","centers","must","be","of","size","num_clusters","x","n"]
code: def _compute_distance(self, centers: np.ndarray) -> float:
      """Sum of minimum squared distances to closest centroid
      centers must be of size num_clusters x n
      """
      return _kmeans_distance(self._points, centers)
  
imports: import numpy
  import pandas
  import scipy.spatial
  from nevergrad.parametrization import parameter
  import nevergrad.common.typing
  from ..base import ExperimentFunction
  from .None import datasets
label_desc: Use image node_id 3 for calling the Clustering obj's underlying member method code with example usage: obj._compute_distance(centers) and returns: _kmeans_distance